The present invention relates to heating, ventilation and cooling (HVAC) systems, and more particularly to fault diagnostics associated with early detection and isolation of failures in HVAC systems.
HVAC systems often do not function as well as expected due to faults developed during routine operation. While these faults are indicative of a failure mode, many faults do not result in immediate system shut down or costly damages. However, most faults, if unnoticed for a long period of time, could adversely affect system performance, life, and lifecycle cost.
While diagnostics refer to detection and isolation of faults, prognostic typically refers to predicting faults before they occur. In many applications, however, early detection and diagnostics may serve the same end as prognostics. This is the case when failure propagation happens at a reasonably slow pace. Small changes in system parameters typically do not have a substantial adverse effect initially. As such, accurate prediction of the time between detection of a fault, that is, a small change to one or more system parameters, to full system deterioration or shutdown is not crucial. For instance, detection of HVAC system refrigerant charge leakage and air filter plugging are examples of failure modes for which early detection of changes provides adequate information to take timely maintenance action.
Approaches to diagnostics may be divided into two broad categories. One category deals with direct measurement of monitored quantities and another category combines sensing technologies with mathematical algorithms. The technical emphasis in these approaches is the development of dedicated sensors for measurement of crucial system parameters. While such approaches may be more accurate, they are typically costly as they involve adding dedicated hardware for each failure mode of interest. In the combined approach, algorithms play the major role since they allow inference about the health of the system from indirect measurements provided by the sensors. Because the addition of new sensors is more expensive and more difficult to manufacture, algorithms alone are incorporated to utilize available sensors that are configured for a control purpose.
Design of failure detection and diagnostics algorithms have been subject of extensive research ranging from statistical approaches and reviews to techniques derived from artificial intelligence and reasoning, graph theory, and bond graphs. Several diagnostics techniques have been applied to the problem of chiller and HVAC fault isolation. Among known approaches, “black-box” or data-driven techniques (such as neural networks) have received considerable attention. Such approaches are well suited to domains where data is abundant but physical knowledge about the phenomenon is scarce. However, one problem with such approaches is that recalibrating the parameters of the black-box model typically requires extensive re-experimentation even if the system changes slightly as there is no direct linkage between model parameters and physical system quantities.
As such, there is a desire for an analytical approach to detect faults which reconciles the results of known data driven techniques with a physical understanding of the HVAC system and provides a direct linkage between model parameters and physical system quantities to arrive at classification rules that are easy to interpret, calibrate and implement.